What is the difference between correlation and causality? What does science say?

Let’s dive right in and describe the main differences between these two common terms.

Correlation Reports: The Solution for Large SEO Analysis

When running an SEO analysis for a high number of factors, the best approach to identify correlations between different factors is to user the correlation algorithm as Spearman, Kendall or Pearson. This allows us to understand if 2 different factors are changing on the same time in the same direction, and to estimate the influence level they might have on each other.

To understand how exactly the Spearman Correlation Algorithm works you shall check the next Youtube video:

Correlation vs Causality

This might seem a simple question to answer. However, many people tend to mix up these two relationship,often causing incorrect conclusions .

Let’s see what each of the two terms mean :

Correlation

A relation between “phenomena or things or between mathematical or statistical variables which tend to vary, be associated, or occur together in a way not expected on the basis of chance alone”,according to Merriam-Webster

Correlation has a value between -1 and 1, where:

1 would be a perfect correlation

0 will be no correlation

-1 is a negative correlation defined as a relationship between two variables in which one variable increases as the other decreases, and vice versa.

This is one of the best ways to establish relationships between variables; it is one of the good methods that helps us find a common link between seemingly unrelated items.

However, it doesn’t tell us enough about values. It is mainly used to group two variables and predict how the first will change when the second changes.

Correlation examples

For example, if they are fully correlated this will imply that the value of first will increase (or decrease) in the same amount (percentage) as the value of second.

Correlation doesn’t only work for site content and SEO but can also be used for statistics, acquiring scientific evidence, risk control, improving technology, health industry and when performing various studies.

Causality

The relation between something that happens and the thing that causes it . The first thing that happens is the cause and the second thing is the effect .

It is very important to know that correlation does not mean causality. While correlation is a mutual connection between two or more things, causality is the action of causing something.

Causality examples

For example, there is a correlation between ice cream sales and the temperature, as you can see in the chart below .

Causal relationship is something that can be used by any company.

As you can easily see, warmer weather caused more sales and this means that there is a correlation between the two. However, we can’t say that ice cream sales cause hot weather (this would be a causation).

Same correlation can be found between Sunglasses and the Ice Cream Sales but again the cause for both is the outdoor temperature.

You have to be careful when observing a certain problem.

For example, this test may imply that people eat more food during summer. This is of course not true as it is obvious that people need to cool off during summer.

To understand better the difference between correlation and causality ,watch this movie .

How can I use correlation and causation for SEO?

This difference must be taken into consideration for SEO analysis when trying to identify the causality between different factors.

Before you can judge certain events you need to view them from different perspectives. As you study different SEO factors, you will learn more about real relationships which will be important for making well-educated guesses.

For example, inexperienced person will think there is a correlation between large amount number of links and higher rankings. However, based on this, any web expert will tell you that this is in fact causality (you get positions because of the links).

As you work more within the field you will able to asses and rate these popular situations.

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